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This study evaluates variational quantum algorithms for the inverse problem of protein design, specifically optimizing amino acid sequences on noisy intermediate-scale quantum devices. The research highlights a critical trade-off where problem-informed circuits struggle with hardware noise, whereas problem-agnostic, hardware-efficient ansätze demonstrate improved robustness and significant success probabilities in real-device experiments. These findings offer practical insights into leveraging current quantum hardware for bio-inspired optimization tasks without requiring full error correction.
Designing lattice proteins with variational quantum algorithms
arXiv:2508.02369v2 Announce Type: replace Abstract: Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where one seeks sequences that fold to a given target structure. The latter problem is often split into two steps: (i) searching for sequences that minimize the energy in the target structure, and (ii) testing whether the generated sequences fold to the desired structure. Here, we investigate the utility of variational quantum algorithms for the first of these two steps on today's noisy intermediate-scale quantum devices. We focus on the sequence optimization task, which is less resource-demanding than folding computations. We test the quantum approximate optimization algorithm and variants of it, with problem-informed quantum circuits, as well as the hardware-efficient ansatz, with problem-agnostic quantum circuits. While the former approach, with a careful choice of mixer, yields good results in noiseless simulations (success probability $\geq$0.95 in all instances), its performance drops drastically under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, improved performance is observed in noisy simulations, compared to their problem-informed counterparts. When running the problem-agnostic circuits on a real quantum device, with parameters taken from simulations, we obtain a significant success probability ($\geq$0.2) in most instances.
Abstract:Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where one seeks sequences that fold to a given target structure. The latter problem is often split into two steps: (i) searching for sequences that minimize the energy in the target structure, and (ii) testing whether the generated sequences fold to the desired structure. Here, we investigate the utility of variational quantum algorithms for the first of these two steps on today's noisy intermediate-scale quantum devices. We focus on the sequence optimization task, which is less resource-demanding than folding computations. We test the quantum approximate optimization algorithm and variants of it, with problem-informed quantum circuits, as well as the hardware-efficient ansatz, with problem-agnostic quantum circuits. While the former approach, with a careful choice of mixer, yields good results in noiseless simulations (success probability $\geq$0.95 in all instances), its performance drops drastically under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, improved performance is observed in noisy simulations, compared to their problem-informed counterparts. When running the problem-agnostic circuits on a real quantum device, with parameters taken from simulations, we obtain a significant success probability ($\geq$0.2) in most instances.
arXiv:2508.02369 [quant-ph]
(or arXiv:2508.02369v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2508.02369 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1103/kpf7-fx7t Focus to learn more DOI(s) linking to related resources
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